单轮对话中多重支持策略建模在情感支持对话中的应用

摘要

情感支持对话（ESC）旨在通过生成具有共情力的支持性对话来帮助处于困境中的个体。现有研究通常默认每个支持话轮仅对应单一策略，而现实中的支持性交流往往在单个话语中融合多种策略。本文通过将ESC任务重构为多策略话语生成——每个话语可包含一个或多个策略-响应对，重新审视了这一任务。我们提出两种生成方法：All-in-One（单步解码预测所有策略-响应对）和One-by-One（迭代生成策略-响应对直至完成）。两种方法均通过强化学习引导的认知推理进行增强，以优化策略选择与响应构建。我们在ESConv数据集上进行了话语级和对话级实验评估，结果表明我们的方法能有效建模多策略话语，显著提升支持质量与对话成功率。据我们所知，本研究首次系统性地实证证明了在单话语中融合多重支持策略对情感支持对话具有可行性和有效性。所有代码与数据将公开于https://github.com/aliyun/qwen-dianjin。

English

Emotional Support Conversation (ESC) aims to assist individuals experiencing distress by generating empathetic and supportive dialogue. While prior work typically assumes that each supporter turn corresponds to a single strategy, real-world supportive communication often involves multiple strategies within a single utterance. In this paper, we revisit the ESC task by formulating it as multi-strategy utterance generation, where each utterance may contain one or more strategy-response pairs. We propose two generation methods: All-in-One, which predicts all strategy-response pairs in a single decoding step, and One-by-One, which iteratively generates strategy-response pairs until completion. Both methods are further enhanced with cognitive reasoning guided by reinforcement learning to improve strategy selection and response composition. We evaluate our models on the ESConv dataset under both utterance-level and dialogue-level settings. Experimental results show that our methods effectively model multi-strategy utterances and lead to improved supportive quality and dialogue success. To our knowledge, this work provides the first systematic empirical evidence that allowing multiple support strategies within a single utterance is both feasible and beneficial for emotional support conversations. All code and data will be publicly available at https://github.com/aliyun/qwen-dianjin.